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@article{202216,
        author = {Badal Kumar and Dharmesh Kumar, and Amit Kumar},
        title = {Analysis of reinforcement learning for security alert in financial system.},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {9893-9899},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202216},
        abstract = {Cyber-attacks, fraud and abnormal transactions pose a huge number of security alerts on financial systems. Conventional rule-based and monitored machine learning techniques are not typically capable of keeping up with new threats, and produce a high false positive, which results in alert fatigue in security analysts. Financial environments are highly dynamic and thus, there is need of smart systems that can learn and get better in the process of making decisions. The paper will examine how reinforcement learning is used in security alerts in financial systems. The process related to security alerts handling is projected as a series of sequential decision-making in which an RL agent acquires the best policies to classify and prioritize alerts by constantly interacting with the surrounding system. The paper emphasizes the role of reinforcement learning in improving adaptability, minimizing false alerts, and increasing the detection rate as compared to the traditional methods, thus showing how it can be used to develop efficient and automated security monitoring solutions. Keywords- Reinforcement Learning, Financial Security, Intrusion Detection, Security Alert Management, Cyber Fraud Detection, Machine Learning, Anomaly Detection, Adaptive Systems.},
        keywords = {},
        month = {May},
        }